





'''simple docstring'''






import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer






def   __A    (				a_    :	str   ,a_    :	int   ,a_    :	Optional[Any]   ,a_    :	Tuple   ,a_    :	Optional[int]=True   ,a_    :	int="pt"						):
		lowerCAmelCase  :     Tuple       						= {"add_prefix_space": True} if isinstance(a_   ,a_						) and not line.startswith(" "						) else {}
		lowerCAmelCase  :     int       						= padding_side
		return tokenizer(
		    [line]   ,max_length=a_   ,padding="max_length" if pad_to_max_length else None   ,truncation=a_   ,return_tensors=a_   ,add_special_tokens=a_   ,**a_   ,)






def   __A    (				a_    :	Dict   ,a_    :	Dict   ,a_    :	Optional[Any]=None   ,):
		lowerCAmelCase  :     int       						= input_ids.ne(a_						).any(dim=0						)
		if attention_mask is None:
				return input_ids[:, keep_column_mask]
		else:
				return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class 		lowerCamelCase	(   _A							):



	def __init__( self		,   a_		,   a_		,   a_		,   a_		,   a_="train"		,   a_=None		,   a_=None		,   a_=None		,   a_=""		,   ):
			super().__init__()
			lowerCAmelCase  :     Union[str, Any]       						= Path(a_						).joinpath(type_path + ".source"						)
			lowerCAmelCase  :     Tuple       						= Path(a_						).joinpath(type_path + ".target"						)
			lowerCAmelCase  :     Tuple       						= self.get_char_lens(self.src_file						)
			lowerCAmelCase  :     str       						= max_source_length
			lowerCAmelCase  :     int       						= max_target_length
			assert min(self.src_lens						) > 0, F'''found empty line in {self.src_file}'''
			lowerCAmelCase  :     Dict       						= tokenizer
			lowerCAmelCase  :     Tuple       						= prefix
			if n_obs is not None:
					lowerCAmelCase  :     Optional[int]       						= self.src_lens[:n_obs]
			lowerCAmelCase  :     List[Any]       						= src_lang
			lowerCAmelCase  :     Tuple       						= tgt_lang



	def __len__( self						):
			return len(self.src_lens						)



	def __getitem__( self		,   a_						):
			lowerCAmelCase  :     List[str]       						= index + 1  # linecache starts at 1
			lowerCAmelCase  :     Tuple       						= self.prefix + linecache.getline(str(self.src_file						)		,   a_						).rstrip("\n"						)
			lowerCAmelCase  :     List[Any]       						= linecache.getline(str(self.tgt_file						)		,   a_						).rstrip("\n"						)
			assert source_line, F'''empty source line for index {index}'''
			assert tgt_line, F'''empty tgt line for index {index}'''

			# Need to add eos token manually for T5
			if isinstance(self.tokenizer		,   a_						):
					source_line += self.tokenizer.eos_token
					tgt_line += self.tokenizer.eos_token

			# Pad source and target to the right
			lowerCAmelCase  :     Tuple       						= (
			    self.tokenizer.question_encoder if isinstance(self.tokenizer		,   a_						) else self.tokenizer
			)
			lowerCAmelCase  :     Dict       						= self.tokenizer.generator if isinstance(self.tokenizer		,   a_						) else self.tokenizer

			lowerCAmelCase  :     List[str]       						= encode_line(a_		,   a_		,   self.max_source_length		,   "right"						)
			lowerCAmelCase  :     Union[str, Any]       						= encode_line(a_		,   a_		,   self.max_target_length		,   "right"						)

			lowerCAmelCase  :     Dict       						= source_inputs["input_ids"].squeeze()
			lowerCAmelCase  :     List[str]       						= target_inputs["input_ids"].squeeze()
			lowerCAmelCase  :     int       						= source_inputs["attention_mask"].squeeze()
			return {
			    "input_ids": source_ids,
			    "attention_mask": src_mask,
			    "decoder_input_ids": target_ids,
			}



	@staticmethod
	def 	_lowerCamelCase     ( a_						):
			return [len(a_						) for x in Path(a_						).open().readlines()]





	def 	_lowerCamelCase     ( self		,   a_						):
			lowerCAmelCase  :     Optional[int]       						= torch.stack([x["input_ids"] for x in batch]						)
			lowerCAmelCase  :     str       						= torch.stack([x["attention_mask"] for x in batch]						)
			lowerCAmelCase  :     Optional[Any]       						= torch.stack([x["decoder_input_ids"] for x in batch]						)
			lowerCAmelCase  :     str       						= (
			    self.tokenizer.generator.pad_token_id
			    if isinstance(self.tokenizer		,   a_						)
			    else self.tokenizer.pad_token_id
			)
			lowerCAmelCase  :     Optional[Any]       						= (
			    self.tokenizer.question_encoder.pad_token_id
			    if isinstance(self.tokenizer		,   a_						)
			    else self.tokenizer.pad_token_id
			)
			lowerCAmelCase  :     Union[str, Any]       						= trim_batch(a_		,   a_						)
			lowerCAmelCase						,		lowerCAmelCase  :     Optional[int]       						= trim_batch(a_		,   a_		,   attention_mask=a_						)
			lowerCAmelCase  :     Optional[Any]       						= {
			    "input_ids": source_ids,
			    "attention_mask": source_mask,
			    "decoder_input_ids": y,
			}
			return batch






lowerCAmelCase									=   getLogger(__name__)






def   __A    (				a_    :	List[List]						):
		return list(itertools.chain.from_iterable(a_						)						)






def   __A    (				a_    :	str						):
		lowerCAmelCase  :     Any       						= get_git_info()
		save_json(a_   ,os.path.join(a_   ,"git_log.json"						)						)






def   __A    (				a_    :	Dict   ,a_    :	Dict   ,a_    :	Any=4   ,**a_    :	int						):
		with open(a_   ,"w"						) as f:
				json.dump(a_   ,a_   ,indent=a_   ,**a_						)






def   __A    (				a_    :	Tuple						):
		with open(a_						) as f:
				return json.load(a_						)






def   __A    (				):
		lowerCAmelCase  :     Dict       						= git.Repo(search_parent_directories=a_						)
		lowerCAmelCase  :     Tuple       						= {
		    "repo_id": str(a_						),
		    "repo_sha": str(repo.head.object.hexsha						),
		    "repo_branch": str(repo.active_branch						),
		    "hostname": str(socket.gethostname()						),
		}
		return repo_infos






def   __A    (				a_    :	Callable   ,a_    :	Iterable						):
		return list(map(a_   ,a_						)						)






def   __A    (				a_    :	Any   ,a_    :	Optional[int]						):
		with open(a_   ,"wb"						) as f:
				return pickle.dump(a_   ,a_						)






def   __A    (				a_    :	Union[str, Any]						):

		def remove_articles(a_    :	Dict						):
				return re.sub(R"\b(a|an|the)\b"   ," "   ,a_						)

		def white_space_fix(a_    :	Any						):
				return " ".join(text.split()						)

		def remove_punc(a_    :	str						):
				lowerCAmelCase  :     Union[str, Any]       						= set(string.punctuation						)
				return "".join(ch for ch in text if ch not in exclude						)

		def lower(a_    :	List[str]						):
				return text.lower()

		return white_space_fix(remove_articles(remove_punc(lower(a_						)						)						)						)






def   __A    (				a_    :	int   ,a_    :	Optional[Any]						):
		lowerCAmelCase  :     List[Any]       						= normalize_answer(a_						).split()
		lowerCAmelCase  :     List[Any]       						= normalize_answer(a_						).split()
		lowerCAmelCase  :     List[str]       						= Counter(a_						) & Counter(a_						)
		lowerCAmelCase  :     Optional[Any]       						= sum(common.values()						)
		if num_same == 0:
				return 0
		lowerCAmelCase  :     int       						= 1.0 * num_same / len(a_						)
		lowerCAmelCase  :     Optional[Any]       						= 1.0 * num_same / len(a_						)
		lowerCAmelCase  :     List[str]       						= (2 * precision * recall) / (precision + recall)
		return fa






def   __A    (				a_    :	List[str]   ,a_    :	Dict						):
		return normalize_answer(a_						) == normalize_answer(a_						)






def   __A    (				a_    :	List[str]   ,a_    :	List[str]						):
		assert len(a_						) == len(a_						)
		lowerCAmelCase  :     Tuple       						= 0
		for hypo, pred in zip(a_   ,a_						):
				em += exact_match_score(a_   ,a_						)
		if len(a_						) > 0:
				em /= len(a_						)
		return {"em": em}






def   __A    (				a_    :	List[Any]						):
		return model_prefix.startswith("rag"						)






def   __A    (				a_    :	int   ,a_    :	int   ,a_    :	Any						):
		lowerCAmelCase  :     int       						= {p: p for p in extra_params}
		# T5 models don't have `dropout` param, they have `dropout_rate` instead
		lowerCAmelCase  :     int       						= "dropout_rate"
		for p in extra_params:
				if getattr(a_   ,a_   ,a_						):
						if not hasattr(a_   ,a_						) and not hasattr(a_   ,equivalent_param[p]						):
								logger.info("config doesn't have a `{}` attribute".format(a_						)						)
								delattr(a_   ,a_						)
								continue
						lowerCAmelCase  :     Tuple       						= p if hasattr(a_   ,a_						) else equivalent_param[p]
						setattr(a_   ,a_   ,getattr(a_   ,a_						)						)
						delattr(a_   ,a_						)
		return hparams, config










'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase									=   logging.get_logger(__name__)

lowerCAmelCase									=   {
    """EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json""",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class 		lowerCamelCase	(   _A							):
	snake_case_   = "gpt_neox"



	def __init__( self		,   a_=50_432		,   a_=6_144		,   a_=44		,   a_=64		,   a_=24_576		,   a_="gelu"		,   a_=0.25		,   a_=10_000		,   a_=0.0		,   a_=0.0		,   a_=0.1		,   a_=2_048		,   a_=0.02		,   a_=1e-5		,   a_=True		,   a_=0		,   a_=2		,   a_=False		,   a_=True		,   a_=None		,   **a_		,   ):
			super().__init__(bos_token_id=a_		,   eos_token_id=a_		,   **a_						)
			lowerCAmelCase  :     Dict       						= vocab_size
			lowerCAmelCase  :     str       						= max_position_embeddings
			lowerCAmelCase  :     int       						= hidden_size
			lowerCAmelCase  :     Optional[Any]       						= num_hidden_layers
			lowerCAmelCase  :     int       						= num_attention_heads
			lowerCAmelCase  :     Any       						= intermediate_size
			lowerCAmelCase  :     Tuple       						= hidden_act
			lowerCAmelCase  :     Any       						= rotary_pct
			lowerCAmelCase  :     str       						= rotary_emb_base
			lowerCAmelCase  :     Tuple       						= attention_dropout
			lowerCAmelCase  :     Optional[int]       						= hidden_dropout
			lowerCAmelCase  :     int       						= classifier_dropout
			lowerCAmelCase  :     str       						= initializer_range
			lowerCAmelCase  :     str       						= layer_norm_eps
			lowerCAmelCase  :     List[Any]       						= use_cache
			lowerCAmelCase  :     Dict       						= tie_word_embeddings
			lowerCAmelCase  :     Any       						= use_parallel_residual
			lowerCAmelCase  :     Union[str, Any]       						= rope_scaling
			self._rope_scaling_validation()

			if self.hidden_size % self.num_attention_heads != 0:
					raise ValueError(
					    "The hidden size is not divisble by the number of attention heads! Make sure to update them!"						)





	def 	_lowerCamelCase     ( self						):
			if self.rope_scaling is None:
					return

			if not isinstance(self.rope_scaling		,   a_						) or len(self.rope_scaling						) != 2:
					raise ValueError(
					    "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
					    F'''got {self.rope_scaling}'''						)
			lowerCAmelCase  :     int       						= self.rope_scaling.get("type"		,   a_						)
			lowerCAmelCase  :     int       						= self.rope_scaling.get("factor"		,   a_						)
			if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
					raise ValueError(
					    F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''						)
			if rope_scaling_factor is None or not isinstance(a_		,   a_						) or rope_scaling_factor <= 1.0:
					raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''						)



